An optimized GMM algorithm and its application in single-trial motor imagination recognition

Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments

Active transportation, such as cycling, improves mobility and general health. However, statistics reveal that in low- and middle-income countries, male and female cycling participation rates differ significantly. Existing literature highlights that women's willingness to use bicycles is significantly influenced by their perception of security. This study employs virtual reality (VR) cycling simulation and electroencephalography (EEG) analysis to investigate factors influencing female cyclists' perceptions of security in Tehran. A total of 52 female participants took part in four scenarios within a VR bicycle simulator, which simulates various environmental settings. In this experiment, participants' brainwave signals are gathered through an EEG device, and a questionnaire with their stated preferences is filled out. The Gaussian mixture approach is used to cluster brainwave patterns based on security perception from EEG data. Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. Consequently, the support vector machine model, with an F1 score of 0.74, appears to be the most effective technique for the classification of environmental and surveillance factors. Furthermore, the SelectKBest algorithm determines that factors such as the presence of obstacles like kiosks, cycling routes passing through tunnels and underpasses, the level of incivility in the urban cycling environment, and the presence of informal surveillance have the biggest impact on female cyclists' security perception.